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ACM Computing Classification System

Here are the categories and subcategories of AI topics from the 2012 ACM Computing Classification System. We give [in bold] the chapter or section(s) in AIMA that cover each topic.

Artificial Intelligence

Natural language processing [Ch. 23, 24]

Information extraction [Sec. 23.6] Machine translation [Sec. 23.6, 24.6] Discourse, dialogue and pragmatics [Sec. 23.5] Natural language generation [Sec. 23.6] Speech recognition [Sec. 23.6] Lexical semantics [Sec. 23.4, 24.1] Phonology / morphology [Sec. 23.1] Language resources [Sec. 24.6]

Knowledge representation and reasoning [Ch. 10]

Description logics [Sec. 10.5] Semantic networks [Sec. 10.5] Nonmonotonic, default reasoning and belief revision [Sec. 10.6] Probabilistic reasoning [Ch. 13-15] Vagueness and fuzzy logic [Sec. 23.1] Causal reasoning and diagnostics [Sec. 13.5] Temporal reasoning [Ch. 14] Cognitive robotics [Sec. 26.8] Ontology engineering [Sec. 10.1] Logic programming and answer set programming [Sec. 9.4] Spatial and physical reasoning [Sec. 10.6] Reasoning about belief and knowledge [Sec. 10.4]

Planning and scheduling [Ch. 11]

Planning for deterministic actions [Sec. 11.1, 11.2] Planning under uncertainty [Sec. 11.5] Multi-agent planning [Ch. 18] Planning with abstraction and generalization [Sec. 11.3, 11.4] Robotic planning [Sec. 26.5]     Evolutionary robotics [Sec. 4.1]

Search methodologies [Ch. 3, 4]

Heuristic function construction [Sec. 3.5] Discrete space search [Ch. 3] Continuous space search [Sec. 4.2] Randomized search [Sec. 19.8] Game tree search [Ch. 5] Abstraction and micro-operators [Sec. 3.6] Search with partial observations [Sec. 4.4]

Control methods [Ch. 26]

Robotic planning [Sec. 26.5]     Evolutionary robotics [Sec. 4.1] Computational control theory [Sec. 26.5] Motion path planning [Sec. 26.5]

Philosophical/theoretical foundations of artificial intelligence [Ch. 27]

Cognitive science [Sec. 1.1] Theory of mind [Sec. 27.2]

Distributed artificial intelligence [Ch. 18]

Multi-agent systems [Ch. 18] Intelligent agents [Ch. 2] Mobile agents [Ch. 26] Cooperation and coordination [Sec. 18.3]

Computer vision [Ch. 25]

Computer vision tasks [Sec. 25.7] Image and video acquisition [Sec. 25.3, 25.6] Computer vision representations [Sec. 25.3] Computer vision problems [Sec. 25.7]

Machine learning [Ch. 19-22]

Supervised learning [Ch. 19]     Ranking [Sec. 18.4]     Supervised learning by classification [Sec. 19.6]     Supervised learning by regression [Sec. 19.6]     Structured outputs [Ch. 19]     Cost-sensitive learning [Sec. 22.3] Unsupervised learning [Sec. 20.3]     Cluster analysis [Sec. 20.3]     Anomaly detection [Sec. 19.9]     Mixture modeling [Sec. 20.2]     Topic modeling [Ch. 23 Notes]     Source separation [N/A]     Motif discovery [N/A]     Dimensionality reduction and manifold learning [Sec. 21.7] Reinforcement learning [Ch. 22]     Sequential decision making [Ch. 17]     Inverse reinforcement learning [Sec. 22.6]     Apprenticeship learning [Sec. 22.6]     Multi-agent reinforcement learning [Sec. 22.7]     Adversarial learning [Sec. 21.7] Multi-task learning [Sec. 21.7]     Transfer learning [Sec. 21.7]     Lifelong machine learning [Ch. 4 Notes]     Learning under covariate shift [Sec. 19.9] Learning settings [Sec. 19.8]     Batch learning [Sec. 21.4]     Online learning settings [Sec. 19.8]     Learning from demonstrations [Sec. 22.6]     Learning from critiques [Sec. 2.4]     Learning from implicit feedback [Sec. 2.4]     Active learning settings [Sec. 22.3]     Semi-supervised learning settings [Sec. 19.9] Machine learning approaches [Ch. 19]     Classification and regression trees [Sec. 19.3]     Kernel methods [Sec. 19.7]         Support vector machines [Sec. 19.7]             Gaussian processes [Sec. 20.3]             Neural networks [Ch. 21]     Logical and relational learning [Sec. 19.7]     Inductive logic learning [Sec. 19.2]     Statistical relational learning [Ch. 20]     Learning in probabilistic graphical models [Ch. 20]         Maximum likelihood modeling [Sec. 20.2]         Maximum entropy modeling [Ch. 20]         Maximum a posteriori modeling [Sec. 20.1]         Mixture models [Sec. 20.3]         Latent variable models [Sec. 20.3]         Bayesian network models [Ch. 20]     Learning linear models [Sec. 19.6]         Perceptron algorithm [Ch. 21 Notes]     Factorization methods [Sec. 19.9]         Non-negative matrix factorization [N/A]             Factor analysis [Sec. 19.9]             Principal component analysis [Sec. 21.7]             Canonical correlation analysis [N/A]             Latent Dirichlet allocation [Ch. 23 Notes] Rule learning [Ch. 22 Notes] Instance-based learning [Sec. 19.7] Markov decision processes [Sec. 17.1] Partially-observable Markov decision processes [Sec. 17.4] Stochastic games [Sec. 18.2] Learning latent representations [Sec. 20.3]     Deep belief networks [Ch. 21] Bio-inspired approaches [Ch. 4 Notes]     Artificial life [Ch. 4 Notes]     Evolvable hardware [N/A]     Genetic algorithms [Sec. 4.2]     Genetic programming [Sec. 4.1]     Evolutionary robotics [Sec. 4.1]     Generative and developmental approaches [Sec. 20.2] Machine learning algorithms [Ch. 19-22]     Dynamic programming for Markov decision processes [Sec. 17.2]     Value iteration [Sec. 17.2]     Q-learning [Sec. 22.3]     Policy iteration [Sec. 17.2]     Temporal difference learning [Sec. 22.2]     Approximate dynamic programming methods [Sec. 22.2]     Ensemble methods [Sec. 19.8]     Boosting [Sec. 19.8]     Bagging [Sec. 19.8]     Spectral methods [N/A]     Feature selection [Sec. 19.4]     Regularization [Sec. 19.4]     Cross-validation [Sec. 19.4]